Many practical control problems feature a team of multiple agents that must coordinate to achieve a common goal [1]}, [2]}.
Cooperative multi-agent reinforcement learning (MARL) has shown considerable promise in solving tasks that can be described as a Dec-POMDP [3]},
i.e., where agents optimise a single scalar team reward signal in a partially observable environment while choosing actions based only on their own local action-observation histories [4]}, [5]}, [6]}.
